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Related Experiment Video

Updated: May 18, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Spatio-temporal auxiliary particle filtering with l1-norm-based appearance model learning for robust visual tracking.

Du Yong Kim1, Moongu Jeon

  • 1School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju 500-712, Korea. duyongkim@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 22, 2012
PubMed
Summary

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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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This study introduces an efficient visual tracker using auxiliary particle filtering and robust subspace learning. The novel approach enhances tracking accuracy and robustness against motion changes and occlusions.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Visual tracking is challenging due to motion changes and appearance variations.
  • Existing methods often suffer from drifting and occlusion issues.
  • Robust object tracking is crucial for various applications like surveillance and autonomous systems.

Purpose of the Study:

  • To propose an efficient and accurate visual tracker.
  • To address drifting problems caused by abrupt motion and appearance variations.
  • To enhance robustness against occlusions and out-of-plane motions.

Main Methods:

  • Utilizing a novel auxiliary particle filtering algorithm with a spatio-temporal sliding window.
  • Implementing a real-time robust principal component pursuit (RRPCP) with l(1)-norm optimization for appearance modeling.

Related Experiment Videos

Last Updated: May 18, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

  • Integrating spatio-temporal filtering and recursive RRPCP for a robust tracking framework.

    • Main Results:

    • The proposed spatio-temporal auxiliary particle filtering is computationally efficient.
    • The RRPCP-based appearance model provides reliable tracking, especially during occlusions.
    • The integrated tracker demonstrates robustness against occlusions and out-of-plane motions.

    Conclusions:

    • The developed visual tracker offers improved accuracy and efficiency.
    • The combination of advanced filtering and appearance modeling overcomes common tracking challenges.
    • Experimental results validate the tracker's effectiveness on challenging video sequences.